How software adds intelligence to the smart grid

Transcription

1 How software adds intelligence to the smart grid by Torben Cederberg, Ventyx and Karen Blackmore, energy and industry analyst Smart grids are seen as a combination of technologies, not just the power products and systems forming the physical transportation of electrical energy. Information technology such as smart meters, advanced SCADA systems, forecasting tools, business intelligence and many other examples of modern software solutions are adding smartness to the power system parts of tomorrow s smart grid networks. The reader will learn from examples dealing with the operation and optimisation of the smart grid but also an example dealing with the smarter asset management of the electrical networks. The latter is of special interest since it is a very evident way of tying investments in the network (enterprise asset management) with the performance of the assets as measured in safety, reliability etc. (network operations). Finally an example of how forecasting and smart data management is used by a utility in Europe will conclude this paper by showing how they are using software adding intelligence to their current network operations. Smart grids are dynamic and information rich As a result of worldwide promotion and investments in green energy as a way of having sustainable growth in the demand for electrical energy, tomorrow s electrical grids need to be smarter than those of today. Why is this? Many smart grids, unlike traditional networks, contain a larger proportion of renewable and distributed generation such as wind and solar power. These new sources of energy production are much more unpredictable since they are dependent on the elements for their production capacity and availability. Traditionally, it was enough to simply predict the load and then size the production accordingly. Adding a dimension by the introduction of volatile energy production in the distribution grid leaves a challenge for the grid operators to operate their networks under much more dynamic and changing conditions. Power flow, as just one example, may change direction and amplitude several times over the course of a day, putting security of power delivery to a real test. Still politicians are putting pressure on the industry to grow its fleet of green power sources, while still keeping tariffs low, and are also increasing pressure for a stable and predictable service. Changes in the power flows of distribution networks is just one smart grid challenge. Another, and perhaps one that is more urgent to resolve, is the huge amount of data produced and readily available, (as depicted by the ones and zeros in Fig. 1). Many utilities already today experience an avalanche of data, most of which is simply stored, but not properly processed. The valuable information is contained in the data, but not made visible or obvious to the utilities. This is where smart grid software can make a significant difference and, used correctly, can turn the massive amount of raw data into invaluable information to help the utilities in making better business decisions and to master the operation and ownership of tomorrow s smart grids. Smart grids require both intelligent power systems as well as intelligent system support. This is quite a challenge for the industry. Understanding what these challenges are is a first step, but what are the components of a blueprint to master the challenges of tomorrow s smart grids? How technology can enable a smarter grid There are three key components that form the blueprint for the most successful smart grids of tomorrow: Intelligence: unlocking the wealth of new data along with existing knowledge to drive actions and automation Integration: optimise the entire electrical value chain in a responsive and responsible manner Innovation: incorporate new technology as it comes about in an agile manner and recognise possibilities as additional knowledge is gained from current implementations These components will enable utilities to adopt collaborative, responsive and heuristic business models that give new meaning to the term smart grid providing the energy for tomorrow while preserving the environment. Intelligence Fig. 1: The smarter grid is rich with data. In order to fully unlock the wealth of new data, utilities are investing in sensors and monitors. The data from these sensors coupled with industry specific key performance indicators, algorithms and existing knowledge form the basis of increasing smart grid intelligence. While big data is often talked about in terms of intelligence, smart utilities will be looking at targeted data. Utilities will want the data to answer questions such as: What do we want to know? Where do we need to learn more about the grid and its functions? How much do we trust the data and the systems within the grid? How do our customers trust the information we supply them and how do they trust us to do what we say we are going to do? What components within the grid are missing that would supply more answers? th AMEU Technical Convention 2013

2 How much do we don t know that we don t know? What is the next step? As a utility continues to drill into the data and gain valuable insights into the significance of applied intelligence, it will unlock answers to the above questions and be able to act upon situations that are happening within the grid. Integration Many utilities currently working on smart grid projects are spending time on information technology, IT and operation technology, OT convergence. Blending these two technologies to provide true integration is a key to the puzzle of connecting the dots in the network. In addition to the integration of the technologies, processes must be integrated to take advantage of the automation that can occur once IT and OT are integrated. These processes allow smart automation to take place without intervention and alerts for responses that require manual intervention, additional decision-making and actions that cannot be completed with the current systems in place. Once the processes are integrated, there is another integration that involves customers and their responses to events such as outages or peak demands and the employees who help the customers understand what is involved. This integration of customers and other stakeholders is as important as the convergence of the technologies. Innovation The third I, innovation, is fed by intelligence and integration. Innovation is what led to astronauts walking on the moon. However, to get to that point, people had to solve the problems of traveling in space and then landing on and returning from the moon. They had to solve problems at Fig. 2: Asset health centre. hand, but had a vision of the greater picture and goal. Utilities today that are innovative about smart grid solutions dream about a better planet and conditions for quality of life and fully sustainable systems for electric resources that supply all people regardless of location or income. But those utilities are aware that the problems of today must be solved first in order to reach the energy needs and dreams of tomorrow. VaasaETT 1, while doing the research and analysing the results from their smart grid global research project, found that innovation was a deciding factor in successful smart grid projects. As we will see next, the three I components, intelligence, integration and innovation are all essential for building the solutions for tomorrow s smart grid applications. What are the solutions that add intelligence to the smart grids? The areas described will all exemplify how software solutions are processing data, in many cases, already existing and readily available, for utilities to form strategies and execute both in asset management as well as in operations in a more optimised way. Several vendors are offering solutions in all mentioned areas. This paper will be describing the general principles in each area and will use examples, nomenclature and illustrations using examples from ABB and Ventyx. Other vendors may offer similar solutions using other names or configurations, however the principles are similar. The utility reader of this paper is encouraged to research what solutions are available and how they can be applied to its unique network and company structure, taking into consideration company (and network size) as well as business impacts. Asset health The value of a utility is very much dependent upon its assets. Many utilities struggle today with a set of assets that are near or even past their end of life data and a lot of money is put into various prolong the equipment lifetime programs. Many of the maintenance programs are still done in a time based manner, and therefore the optimal usage of the power equipment may not be achieved. The good news is that there is a better way an asset health approach to maintenance. Today, asset maintenance approaches typically rely on an ad-hoc mix of information from multiple sources like time and usage based inspection data, alarms from remote sensors, and industrial enterprise systems. All this data tends to get lost in different organisational silos. Often, utilities rely heavily on human experts to manually review this data, identify trends, and address the risks of the asset portfolio. As more of these experts retire, utilities will need to preserve this expertise as part of the ongoing maintenance data process. Asset health centre merges the subject matter expert knowledge with historical and realtime data. By integrating an organisations operational technology (OT) with its information technology (IT), organisations have the ability to consolidate the wealth of data on systems loads, markets, inspections and equipment sensors. This solution helps identify trends with industry-leading performance models that capture decades of experience building and maintaining critical equipment. Asset health centre doesn t just alert the utility when assets are about to fail, it also helps anticipate issues before they turn into problems. In practice, asset health centre will lead to a state of knowledge where the most valuable assets are mapped and categorised according to their current states and expected lifetime. Proactive maintenance is part of this as well as risk-based decisions of updating, prolonging life time or exchange of equipment is performed. The result is a reduction in risk of critical asset failure by combining many types of asset data to form predictive and actionable intelligence around the health of critical assets. Using this improved level of insight, utilities can take action now and prioritise their response and resources. Organisations can optimise their maintenance spend to get the most from their assets and budgets, build business cases for repair/replace decisions and codify the expertise of their staff in order to make it accessible for new employees and maintenance outsource partners. Note 1: VasaETT, Smart Grid Global Impact Report 2013, th AMEU Technical Convention 2013

3 Outage lifecycle management The relationship the utility has with its customers is often just as important as the health of its assets. Customers value a reliable (and inexpensive!) service of uninterrupted electrical power and if it has to be interrupted for maintenance reasons they will want to know for how long and be able to trust this information. Outage handling, whether planned or unplanned, easily stands out among the most commonly performed tasks by operators in any control room of a modern utility. The difference between working with planned outages and unplanned outages is quite significant. In the first instance, the operator knows what is planned and lies ahead and the challenge is to optimise the work meeting deadlines and budget. In the latter instance, outage management in terms of system restoration is quite different with a much more stochastic process. What the two share is the need to make quick decisions minimising the number of affected customers when doing maintenance and system restoration works. It also needs to keep customers (and other stakeholders) informed of what is going on and when normal service will be resumed. Luckily for the utilities, but also for their customers, planned outages by far outnumber unplanned outages. Still the overall goal of restoring any outage remains the same, which is to resume service quickly and safely for improved customer satisfaction and reduced outage duration and frequency. How can this be done in the most efficient way and what is the difference in doing this in a more conventional network compared with a smart grid environment? An outage usually follows this life cycle: planning pre-event preparations restoration closeout. Also in most, if not all, utilities these life cycles are managed by different people using various computer systems. So a first attempt would naturally be to map these systems and the flow of data to see how this can be smartened up and made more efficiently. We have done extensive studies on this together with a few selected solution partners and drawn a map (Fig. 4). This illustrates a typical map with the different functions performed during an outage life cycle mapped on the horizontal axis while the utility departments supporting the (job) functions are mapped on the vertical axis. Highlighted using different colours are the point solutions that perform the various functions. We can see that the management of outages, both planned and unplanned, is quite complicated and involves many steps, people and exchanges of data. Wouldn t it be a nice idea to put all these functions into one single system or solution? There are vendors that are about to do this by integrating its point solutions and already in the research and development stage of a solution develop it as a part in a bigger picture. Naturally the point solutions can still be used as standalone tools and also integrated with other vendor tools using third party integrators in the classical manner. The take-away from this is that now for the first time there is also the option of a much more holistic solution that will have advantages when it comes to both capacity and more importantly, efficiency, compared with traditional build solutions. The second part of the question related to outage management in smart grid versus conventional networks, what s the difference? First of all, most networks are still traditional so there is not a lot of field experience yet but through the early work of our innovation partners we have gained insights of problems that are foreseen when moving forward into the smart grid network structures. The most obvious challenge is the distributed renewable resources introduced in or near the edge of the grid. An outage is no longer only affecting the disconnection of loads but possibly also production sources (PV-panels and smaller wind generators), making an even more complicated impact to the system restoration or outage planning process. The real-time analysis of the optimal vs. actual power flow must now be taken into account as well as safety precaution when having islands of the network perhaps still being energised. Again the solution to this is even more integration and innovative sharing of data. As this tends to get more and more complex there is also a need to build safety and rule based management into the systems responsible for managing outages. Outage management is actually part of a bigger scheme called distribution system optimisation which is taking the same principles to the total distribution system management. Distribution system optimisation The ability to apply predictive analytics to a combination of operational and information system data helps enable control room operators to have better awareness of capacity and demand situations. The key is to better manage peak demand. Continually increasing demand places additional strain on the aging grid infrastructure. Much of this demand is relatively short (peak) duration such as a cold snap and may not require additional long term investment in generation if other measures can be applied. In 2006, a study was performed under the Independent Electricity System Operator, in USA, which showed that the top 2 GW of load were served Fig. 3: Outage life cycle. Fig. 4: Functional map of outage management system support. 24th AMEU Technical Convention

4 during only 32 system hours (less than 0,4% of the time), underlining that the large amount of capacity that is used is very infrequently. There are a number of strategies available to utilities looking for innovative solutions to this issue. One smart approach involves attacking the issue from both sides managing the peak using conservation voltage reduction, and bringing new capacity online through distributed generation and demand response. These strategies are brought together under the distribution system optimisation solution. Conservation voltage control Where regulation permits, conservation voltage technologies offer utilities at the forefront of technology significant advantages. During pilots in a large North American utility, their estimates placed reduction in peak demand at between 2 4% correlating to a saving of $80-million per year. Historical voltage control methods have had significant limitations. Local-based controls were highly labour intensive and had difficulty allowing operators to take changing network conditions into account. Centralised radiocontrolled systems did not permit systematic optimisation of voltage and var controls for maximum effectiveness in loss reduction and also involved significant human oversight. In contrast, model-based volt/var optimisation utilises mathematical optimisation supported by detailed network modeling and customer load modeling. The benefit of utilising a dynamic model is that the volt/var optimisation always uses the as-operated state of the network. As outages and system reconfigurations occur, the controls adapt to maximise conservation benefits. The savings in deferred generation plants or capacity procurement costs, lower system losses, lower customer energy consumption, and reduced operating and maintenance costs results in model-based volt/var optimisation having one of the strongest business cases for smart grid functionality. Distributed energy resources Distributed energy resources can alleviate overloaded areas and reduce the impact of critical peaks. To facilitate streamlined utilisation of distributed generation, utilities need ways to manage registered resources, forecast requirements, and bring resources online. For example, a peak demand time during a summer heat wave may require generation above normal demand response and local wind farms may need to be brought online if wind is projected or excess PV generation from the local school. With the integration of information technology used to manage the requirements and operational technology to manage on-boarding of the resources, utilities can quickly activate these "virtual power plants". Distributed energy management systems provide real-time tracking, forecasting and aggregations of demand response and distributed energy resources into virtual power plants, enabling improved forecasting and optimisation of these assets. Demand response Fig. 5: VPP/DR signaling for peak clipping. Effective demand response management solutions need to communicate with demand response devices, signaling an event and gathering data. Demand response management system (DRMS) supports commercial and retail utility operations that are required to deliver effective demand response programs and distributed energy management for their smart grid deployments. Using the demand response technology, utilities can give their customers the ability to make more informed choices about how and when they use power by providing them with incentives for controlling energy loads on the network. This enables utilities to better manage peak demand periods, minimise the impact of outages and decrease investments in additional generation, transmission and distribution assets. DRMS solutions communicate with demand response devices, signaling an event and gathering actual data. DRMS enable utility portfolio optimisation by supporting unit commitment and dispatch for complex portfolios that include the full range of portfolio components, including generation, storage, renewables, virtual power plants and complex contracts but also industrial and residential loads. When many units are collectively signaled and managed from a single point it is often referred to as a virtual power plant, VPP. Fig. 6: Forecasting predicts events in the near future. Demand response dashboards facilitate th AMEU Technical Convention 2013

5 the speed of decision making to dispatch distributed energy resources and provide a comprehensive portfolio view. Finally, a DRMS management solution should include an operations dashboard that brings all portfolio elements into a framework to help final decision making as well as reporting on forecast and execution results. Utility example The E.ON smart grid project in short: The situation: The company is one of the world's largest investor-owned power and gas companies serving more than 26-million customers in over 30 countries. The challenge: The grid company in Sweden servicing a geographically very large and disperse network with close to 1-million network customers, needed to prevent instability issues, minimise grid losses, and reduce operator stress by increasing the situation awareness ahead of time. The answer: Combining information (IT) and operational technologies (OT) in the smart grid control centre (SGCC) will deliver a higher degree of grid automation, sensing and visibility; achieve greater control of distributed generation; and further support regulatory compliance. Innovative, integration and intelligence it s all there One of the innovative parts of this project is to work with scenario based simulations of what is foreseen to happen in the near future. The idea is very much trying to address the challenge of wait and see what happens and then react. Monitoring alarms and then react is fine for some processes in the traditional grid but in the much more dynamic smart grids it is not good enough. Fig. 6 illustrates the closed loop control process that will be implemented in this project. What building blocks are needed to do this type of advanced forecasting and analysis in real-time? First of all we need a well-functioning SCADA system with a high resolution of measuring points in the grid. The next crucial building block is the network model (simulator) with network calculations being performed continuously in the background to simulate the real network. It is this simulator that runs the different scenarios and tests the what-ifs being ordered by the operators. When we have the network components in place and integrated, we need behavioral data. Weather, load and generation forecasts need to be feed into the system, this data is the input. Based on the topology engine and the forecast data, the simulator is able to run the network for two hours or longer and check for predicted events and also suggest corrective actions. Can a possible overload in a feeder be detected based on forecasted data? What if a generator is down during high load conditions? Can we allow a temporary overload of 10% at this cable for two hours in the afternoon? All these and many more questions could be answered with a fair precision and therefore make the operators run the network more efficiently and adding capacity and reliability without additional network investments. Smart grid intelligence solutions like this allows network companies to optimise their distribution systems at a very moderate investment compared with adding additional power system components. Software can prove very effective when the necessary data is made available and correctly analysed. Summary and take away Software solutions can add intelligence and produce significant value for utilities planning for the next generation of smart grids. There are three key components that form the blueprint for the most successful smart grids of tomorrow: Intelligence Integration Innovation The blueprint contains the increased use of advanced software solutions. Turning the vast amount of data into actionable insights is a big challenge for any utility. Areas of smart grid applications to consider for rapid deployment are: asset health solutions, distribution system optimisation and outage Fig. 7: Block diagram of the solution. lifecycle management. These areas address some of the top challenges of tomorrow s smart grid and the best software solutions combine intelligence, integration and innovation components to form a holistic solution. Utilities have a lot to gain investing in these solutions starting already today. The process is not a one time, big bang, project but rather a transformational process with many smaller steps. A first step is to map existing system support and data flows. The market is offering several solutions from different vendors. Even if the challenges are global, the implementations can be slightly different locally, with Europe and USA being early out but soon to be followed by utilities in other parts of the world. The solutions discussed cover both asset management (asset investment optimization) as well as the efficient and reliable operation of the network. Because of this it is highly recommended utilities form a companywide strategy before engaging in any point solution. Having this strategy in place and executing accordingly, will lead utilities to additional business gains including; more agile network operations handling the dynamic power flows, servicing the customers with a more predictable and reliable outage management and deferring additional investments while using existing capacity closer to its limits. Contact Toben Cederberg, Ventyx, torben.cederberg@ventyx.abb.com th AMEU Technical Convention 2013